Knowledge-Based Organization Evaluation

Knowledge has become the main value driver for organizations nowadays. In particular, knowledge-based organizations (KBOs) allocate resources for intangible assets (e.g., R&D) in the rapidly changing and highly competitive environment in order to gain competitive advantages. Therefore, how to evaluate knowledge-based organizations has become one of the most important issues in knowledge management. This paper aims to provide a framework for the evaluation of KBOs under uncertainty, using the state-ofthe- art methodology in Real Options. We specify the unique features of KBOs and explain their value drivers. This paper makes three contributions: (1) it bridges the gaps existent in the knowledge management literature on evaluating knowledge capital, (2) it provides a systematic application of Real Options models in the context of knowledge-based organization evaluation, (3) it uses a real world case to demonstrate the implications of the main findings for management

Management of ERP implementation

Implementation of Enterprise Resource Planning (ERP) solutions, which involve both technical and social uncertainties, is in practice a highly uncertain, risky endeavour. Traditional ERP practices address implementation of ERP as a static process; such practices focus on structure, not on ERP as something that will meet the needs of a changing organization. As a result, many relevant uncertainties that cannot be predefined are not easily accommodated. Options theory, which addresses uncertainties over time, resolves uncertainties in changing environments that cannot be predefined. In this paper, we propose an options perspective on the ERP implementation process with a focus on uncertainty. This perspective takes into consideration the often-changing nature of the companies that undertake ERP implementations. In addition, we present a practical example that demonstrates how to use options theory in context, enabling active management when implementing ERP. By actively managing ERP implementation, management can improve the flexibility of ERP implementation and can take appropriate actions to respond to the changing ERP implementation environment, to achieve more a successful ERP implementation that better meets the needs of the organization

Genomic regions, cellular components and gene regulatory basis underlying pod length variations in cowpea (V. unguiculata L. Walp).

Cowpea (V. unguiculata L. Walp) is a climate resilient legume crop important for food security. Cultivated cowpea (V. unguiculata L) generally comprises the bushy, short-podded grain cowpea dominant in Africa and the climbing, long-podded vegetable cowpea popular in Asia. How selection has contributed to the diversification of the two types of cowpea remains largely unknown. In the current study, a novel genotyping assay for over 50 000 SNPs was employed to delineate genomic regions governing pod length. Major, minor and epistatic QTLs were identified through QTL mapping. Seventy-two SNPs associated with pod length were detected by genome-wide association studies (GWAS). Population stratification analysis revealed subdivision among a cowpea germplasm collection consisting of 299 accessions, which is consistent with pod length groups. Genomic scan for selective signals suggested that domestication of vegetable cowpea was accompanied by selection of multiple traits including pod length, while the further improvement process was featured by selection of pod length primarily. Pod growth kinetics assay demonstrated that more durable cell proliferation rather than cell elongation or enlargement was the main reason for longer pods. Transcriptomic analysis suggested the involvement of sugar, gibberellin and nutritional signalling in regulation of pod length. This study establishes the basis for map-based cloning of pod length genes in cowpea and for marker-assisted selection of this trait in breeding programmes

Deep learning for dense Z-spectra reconstruction from CEST images at sparse frequency offsets

A direct way to reduce scan time for chemical exchange saturation transfer (CEST)-magnetic resonance imaging (MRI) is to reduce the number of CEST images acquired in experiments. In some scenarios, a sufficient number of CEST images acquired in experiments was needed to estimate parameters for quantitative analysis, and this prolonged the scan time. For that, we aim to develop a general deep-learning framework to reconstruct dense CEST Z-spectra from experimentally acquired images at sparse frequency offsets so as to reduce the number of experimentally acquired CEST images and achieve scan time reduction. The main innovation works are outlined as follows: (1) a general sequence-to-sequence (seq2seq) framework is proposed to reconstruct dense CEST Z-spectra from experimentally acquired images at sparse frequency offsets; (2) we create a training set from wide-ranging simulated Z-spectra instead of experimentally acquired CEST data, overcoming the limitation of the time and labor consumption in manual annotation; (3) a new seq2seq network that is capable of utilizing information from both short-range and long-range is developed to improve reconstruction ability. One of our intentions is to establish a simple and efficient framework, i.e., traditional seq2seq can solve the reconstruction task and obtain satisfactory results. In addition, we propose a new seq2seq network that includes the short- and long-range ability to boost dense CEST Z-spectra reconstruction. The experimental results demonstrate that the considered seq2seq models can accurately reconstruct dense CEST images from experimentally acquired images at 11 frequency offsets so as to reduce the scan time by at least 2/3, and our new seq2seq network contributes to competitive advantage

An Overview of Plant Phenolic Compounds and Their Importance in Human Nutrition and Management of Type 2 Diabetes

In this paper, the biosynthesis process of phenolic compounds in plants is summarized, which includes the shikimate, pentose phosphate and phenylpropanoid pathways. Plant phenolic compounds can act as antioxidants, structural polymers (lignin), attractants (flavonoids and carotenoids), UV screens (flavonoids), signal compounds (salicylic acid and flavonoids) and defense response chemicals (tannins and phytoalexins). From a human physiological standpoint, phenolic compounds are vital in defense responses, such as anti-aging, anti-inflammatory, antioxidant and anti-proliferative activities. Therefore, it is beneficial to eat such plant foods that have a high antioxidant compound content, which will cut down the incidence of certain chronic diseases, for instance diabetes, cancers and cardiovascular diseases, through the management of oxidative stress. Furthermore, berries and other fruits with low-amylase and high-glucosidase inhibitory activities could be regarded as candidate food items in the control of the early stages of hyperglycemia associated with type 2 diabetes

The Main Progress of Perovskite Solar Cells in 2020–2021

Perovskite solar cells (PSCs) emerging as a promising photovoltaic technology with high efficiency and low manufacturing cost have attracted the attention from all over the world. Both the efficiency and stability of PSCs have increased steadily in recent years, and the research on reducing lead leakage and developing eco-friendly lead-free perovskites pushes forward the commercialization of PSCs step by step. This review summarizes the main progress of PSCs in 2020 and 2021 from the aspects of efficiency, stability, perovskite-based tandem devices, and lead-free PSCs. Moreover, a brief discussion on the development of PSC modules and its challenges toward practical application is provided

Modeling and experiment of a multiple-DOF piezoelectric energy harvester

Vibration energy harvesters have been usually designed as single-degree-of-freedom (1DOF) systems. The fact that such harvesters are only efficient near sole resonance limits their applicability in frequency-variant and random vibration scenarios. In this paper, a novel multiple-DOF piezoelectric energy harvester model (PEHM) is developed, which comprises a primary mass and n parasitic masses. The parasitic masses are independent of each other but attached to the primary mass. The piezoelectric element is placed between the primary mass and the base for energy generation. First, a 2DOF model is analyzed and characterized. Through parametric analysis, it is found that with a slight increase of the overall weight to the original 1DOF harvester (without parasitic masses), two close and effective peaks or one effective peak with significantly enhanced magnitude can be achieved in the power response. Subsequently, the 2DOF model is generalized to an n-DOF model and its analytical solution is derived. This solution provides a convenient tool for parametric study and design of a multiple-DOF piezoelectric energy harvester (PEH). Useful multimodal energy harvesting can be achieved with a slight increase of the overall weight. Finally, a prototype of the proposed multiple-DOF model is devised for proof of concept